The Evolution of Context Windows: Why Bigger Isn’t Always Enough
Over the past two years, we’ve witnessed remarkable growth in large language model context windows. GPT-4 moved from 8K to 32K tokens. Claude expanded to 100K, then 200K. Gemini announced 1 million tokens. This rapid expansion has opened new possibilities—but it hasn’t solved the large-context document problem.
The Growth Trajectory
2022-2023: Most models operated at 2K-8K tokens
- Suitable for short documents and conversations
- Required extensive summarization for longer content
- Limited multi-document analysis
2023-2024: 32K-128K became standard
- Enabled processing of medium-length documents
- Supported extended conversations
- Made some multi-document tasks feasible
2024-2025: 1M+ token windows emerged
- Handling of books and long reports
- Extended code repositories
- Comprehensive document sets
Why This Still Isn’t Enough
Real-World Document Sizes
Consider actual enterprise documents:
Legal Transactions
- Purchase agreements: 200-500 pages
- Exhibits and schedules: 500-2,000 pages
- Due diligence documents: 5,000-50,000 pages
- Referenced materials: unlimited
Construction Projects
- Project specifications: 2,000-5,000 pages
- Contract documents: 500-1,000 pages
- Submittals: 10,000-100,000 pages
- RFIs and correspondence: unlimited
Systems Engineering
- Platform requirements: 1,000-5,000 pages
- Subsystem specs: 10,000-50,000 pages
- Interface control documents: 5,000-20,000 pages
- Test and verification: 20,000-100,000 pages
Even with 1M token windows (~750,000 words or ~1,500 pages), we’re far short of what enterprises need.
The Cost Factor
Larger context windows don’t just consume more tokens—they consume them at every interaction. A single query against 1M tokens can cost:
- $10-30 per query (depending on model)
- Longer processing times
- Increased latency
- Higher computational overhead
For applications requiring hundreds or thousands of queries, this becomes prohibitively expensive.
The Attention Problem
LLMs don’t maintain perfect attention across their entire context window. Research shows:
- Attention degradation in the middle of long contexts
- “Lost in the middle” phenomenon
- Reduced accuracy for information far from query
- Performance varies by context position
Simply fitting everything in the window doesn’t guarantee effective use of that information.
The Solution: Intelligent Context Management
Rather than brute-forcing documents into ever-larger context windows, sophisticated context management techniques provide better results at lower cost:
Retrieval-Augmented Generation (RAG)
Deliver only relevant sections to the model, reducing context size by 10-100x while maintaining or improving accuracy.
GraphRAG
Build knowledge graphs capturing relationships, enabling reasoning across documents without loading everything.
Multi-Layer Summarization
Create hierarchical understanding from overview to detail, navigating to precisely the right level for each query.
Adaptive Approaches
Learn from query patterns and optimize context delivery dynamically.
The Hybrid Future
The future isn’t “large context windows OR intelligent retrieval”—it’s both:
- Use expanded windows for critical documents requiring holistic understanding
- Apply RAG and other techniques to pre-filter from massive document sets
- Reserve expensive full-context processing for high-value queries
- Optimize cost, latency, and accuracy for your specific use case
Conclusion
Context window growth is exciting and valuable. But for enterprise-scale document problems, it’s one tool in a larger toolkit. The organizations succeeding with large-context AI are those combining expanded windows with sophisticated context management techniques.
That’s where TeraContext.AI focuses: not just using bigger hammers, but choosing the right tool for each problem.
Ready to go beyond context limits? Contact us for a free assessment of your document pipeline.